Internet of Things (IoT) sensors predominantly provide visibility to an operating stack – enabling access to real-time and accurate operational data. Laying analysis on top of that data produces dashboards and other visual representations but () extends this further by harnessing the data streams to train models and identify patterns.
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Observations can then be made by a computer much like a human analyst could but at tremendous speed and scale. makes it possible to anticipate and predict events in a robust and scalable way. This can create huge business advantages. In this article, we’ll look at applications of and IoT in construction.
Using IoT technologies to transform the construction industry creates pathways to higher productivity, quality, and safety in the construction processes together with lower environmental impact, and to increased sustainability, fitness-for-use and resilience in the resulting infrastructure. While notable gains are being made possible by the full deployment of IoT, in the longer term it is data-driven techniques and models based on and its subsets (, , and computer vision) that will deliver lasting value in the construction industry.
It is desirable to improve construction processes and save time on menial or time-consuming tasks in addition to reducing the physical workload on the construction site through automation and mass customization thereby removing barriers to cost reduction in the industry. Optimization remains an issue across the value chain, from design to demolition. Procurement rules especially in the public sector have been static over the years and are still restricted to the selection of the lowest bid and leave too little room for creativity and innovations, thus slowing the rate of return on experience and learning curves.
The potential applications of () in construction are broad. With requests for information, open issues, change orders, equipment and worker monitoring, materials delivery, claims management, progress tracking and production analysis as standard in the industry, is like a smart assistant that can scrutinize this mountain of data and alerts project managers about the critical things that need their attention and also highlight easy levers to pull so as to improve outcomes.
Potential Applications of and IoT in Construction
use cases in construction are gaining market traction and attention; from geotechnical engineering for a soil analysis to throwaway sensors and algorithms that can more accurately predict concrete curing times.
A few early-stage examples for firms to take advantage of the promise of range from lowering project costs and driving efficiency, ensuring on-schedule production and delivery, remote performance monitoring, fast dispute resolution, and improving safety and compliance.
Schedule optimization can consider a wide variety of alternatives for project delivery and continuously enhance overall project planning. Using IoT sensors for remote monitoring of construction equipment coupled with the ability to perform analytics of status and location can be employed to reduce costs, improve energy efficiency and limit the idle time of machines. An technique like reinforcement learning, which allows algorithms to learn based on trial and error, has the potential to improve project planning and scheduling through enabling the assessment of endless combinations and alternatives based on similar projects, optimizing the best path and self-correcting over time. Additionally, the increased prevalence of modularization and prefabrication in project delivery results in the movement of large quantities of materials to job sites. This makes the need for enhanced supply chain coordination through supervised learning applications critical to control costs and overall cash flows.
Predictive applications can forecast project risks, constructability, and the structural stability of various technical solutions, providing insight during the decision-making phase and potentially saving millions of dollars down the road. Furthermore, these applications can enable the testing of various materials, limiting the downtime of certain structures during an inspection. The information gathered through data collection and analytics can be leveraged to speed up processes, reduce costs, improve energy efficiency, and apply computer vision to track defects in critical structures. Enhanced analytics platforms can collect and analyze data from sensors to understand signals and patterns to deploy real-time solutions, cut costs, prioritize preventative maintenance, and prevent unplanned downtime.
Increased Efficiency and Improved Productivity
Consistent reduction in waste (i.e. fuel, electricity, water) through real-time monitoring and analysis enables improvement of energy efficiency throughout the construction phases. Moreover, digitalization can be seen as a means of increasing turnover thanks to enhanced productivity and higher customization. Digital technologies like IoT and can help companies provide better products or services with fewer resources. Data analysis plays a central role in this, resulting in an in-depth understanding of the ecosystem to optimize processes and the use of machines. The extensive use of IoT to monitor construction processes and critical data analysis in every phase can lead to better management of resources resulting in a reduction in costs and an increase in profits. […]